How uncertainty can save measurement from circularity and holism

Ritson, Sophie; Staley, Kent W.

doi:10.1016/j.shpsa.2020.10.004

Cited by 10 publications

(9 citation statements)

References 15 publications

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“…Beauchemin also notes, rather interestingly, that in cases of small uncertainties, the allegedly vicious circularity of theory-ladenness in observation is not problematic, precisely because 'its impact on the physics conclusions will be small and fully accounted for' (ibid., p.303). Beauchemin's remarks have been further elaborated by Ritson and Staley (2020) who nicely illustrate how the identification of the assumptions on which a result depends and the further quantification of the dependence of this result on the various assumptions in terms of uncertainty calculations, jointly control the possibility of a vicious circularity at the practical level. The determination of the dependence of the result on the various theoretical assumptions in terms of uncertainty serves in discriminating amongst those model assumptions that have the highest impact on the uncertainty of the result and those whose variation introduces negligible changes.…”

Section: Uncertainty Calculationsmentioning

confidence: 99%

“…In particular, the example of the experimental tests of LFU in rare B-decays nicely illustrates that theory-ladenness emerges at three different levels throughout the experiment depending on the scope and the purpose for which background theoretical assumptions guide the experimental process. The possibility of a vicious circularity due to the theory-ladenness of observation will not be discussed in detail here since this is a well-known problem which has already been thoroughly examined by many authors (Franklin et al, 1989;Brown, 1993;Brewer & Lambert, 2001;Schindler, 2013;Franklin, 2015;Beauchemin, 2017;Ritson & Staley, 2020). The consensus in these discussions is that theory-ladenness is not necessarily vicious and does not lead to a relativist account of contemporary science.…”

Section: Introductionmentioning

confidence: 99%

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What is a data model?

Antoniou

2021

Euro Jnl Phil Sci

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Many decades ago Patrick Suppes argued rather convincingly that theoretical hypotheses are not confronted with the direct, raw results of an experiment, rather, they are typically compared with models of data. What exactly is a data model however? And how do the interactions of particles at the subatomic scale give rise to the huge volumes of data that are then moulded into a polished data model? The aim of this paper is to answer these questions by presenting a detailed case study of the construction of data models at the LHCb for testing Lepton Flavour Universality in rare decays of B-mesons. The close examination of the scientific practice at the LHCb leads to the following four main conclusions: (i) raw data in their pure form are practically useless for the comparison of experimental results with theory, and processed data are in some cases epistemically more reliable, (ii) real and simulated data are involved in the co-production of the final data model and cannot be easily distinguished, (iii) theory-ladenness emerges at three different levels depending on the scope and the purpose for which background theory guides the overall experimental process and (iv) the overall process of acquiring and analysing data in high energy physics is too complicated to be fully captured by a generic methodological description of the experimental practice.

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Section: Uncertainty Calculationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What is a data model?

Antoniou

2021

Euro Jnl Phil Sci

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“…This introduces sources of potential error from incorrect modelling to malfunctioning hard-and software. Finally, (virtually) every CS makes use of parameters that have to be fixed empirically, and this raises the question of how these parameters influence the epistemic status of corresponding experimental results and the models used to facilitate them (Hasse and Lenhard, 2017;Gueguen, 2019;Ritson and Staley, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…1-2). The resulting probability-weights (called 'parton density functions'), furthermore, can only be obtained by fitting parametric functions to a broad range of data (also Staley, 2020;Ritson and Staley, 2020). Similarly, shower models utilize an approximate QCD-result as the basis of a Markovchain in which partons are radiated at distinct times (cf.…”

Section: Introductionmentioning

confidence: 99%

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Why Trust a Simulation? Models, Parameters, and Robustness in Simulation-Infected Experiments

Boge¹

2024

The British Journal for the Philosophy of Science

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Computer simulations are nowadays often directly involved in the generation of experimental results. Given this dependency of experiments on computer simulations, that of simulations on models, and that of the models on free parameters, how do researchers establish trust in their experimental results? Using high energy physics (HEP) as a case study, I will identify three different types of robustness that I call conceptual, methodological, and parametric robustness, and show how they can sanction this trust. However, as I will also show, simulation models in HEP themselves fail to exhibit a type of robustness I call inverse parametric robustness. This combination of robustness and failures thereof is best understood by differentiating different epistemic capacities of simulations and different senses of trust: Trusting simulations in their capacity to facilitate credible experimental results can mean accepting them as means for generating belief in these results, while this need not imply believing the models themselves in their capacity to represent an underlying reality.* This is a preprint version of a paper accepted for publication in British Journal for the Philosophy of Science. The final version includes minor changes.

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The Positive Argument Against Scientific Realism

Boge

2023

J Gen Philos Sci

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Putnam coined what is now known as the no miracles argument “[t]he positive argument for realism”. In its opposition, he put an argument that by his own standards counts as negative. But are there no positive arguments against scientific realism? I believe that there is such an argument that has figured in the back of much of the realism-debate, but, to my knowledge, has nowhere been stated and defended explicitly. This is an argument from the success of quantum physics to the unlikely appropriateness of scientific realism as a philosophical stance towards science. I will here state this argument and offer a detailed defence of its premises. The purpose of this is to both exhibit in detail how far the intuition that quantum physics threatens realism can be driven, in the light also of more recent developments, as well as to exhibit possible vulnerabilities, i.e., to show where potential detractors might attack.

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How uncertainty can save measurement from circularity and holism

Cited by 10 publications

References 15 publications

What is a data model?

What is a data model?

Why Trust a Simulation? Models, Parameters, and Robustness in Simulation-Infected Experiments

The Positive Argument Against Scientific Realism

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